Saturated

Benchmarks are the primary measure of AI capability. They involve testing the most advanced models and seeing what kinds of problems they can solve, or what kinds of human tasks they might be able to do.And from late 2025 to mid-2026, most of the main benchmarks became saturated, meaning the models score so highly that the tests aren't meaningful anymore, both in terms of comparing different models' performance as well as their individual performance. That might suggest the models are just getting good at taking these tests. Or it might mean we're approaching the threshold of AGI.In this episode, we'll hear from Håvard Ihle, who came up with his own benchmark called Weird ML to try to answer this question. Note: Håvard's views are his own and do not represent the views of his employer the Norwegian Defence Research Establishment.The METR time-horizon exponential graph is important context for this episode: https://metr.org/time-horizons/Learn more about WeirdML: https://epoch.ai/benchmarks/weirdmlhttps://www.lesswrong.com/posts/LfQCzph7rc2vxpweS/introducing-the-weirdml-benchmarkhttps://www.lesswrong.com/posts/NLnGRDRXATW2pqXuE/is-the-gap-between-open-and-closed-models-growing-evidencehttps://www.lesswrong.com/posts/ifSBamvobbyB9KWjK/inference-costs-for-hard-coding-tasks-halve-roughly-everyhttps://www.lesswrong.com/posts/hoQd3rE7WEaduBmMT/weirdml-time-horizons